Generative Adversarial Networks Quotes

We've searched our database for all the quotes and captions related to Generative Adversarial Networks. Here they are! All 9 of them:

That we are in the midst of crisis is now well understood. Our nation is at war, against a far-reaching network of violence and hatred. Our economy is badly weakened, a consequence of greed and irresponsibility on the part of some, but also our collective failure to make hard choices and prepare the nation for a new age. Homes have been lost; jobs shed; businesses shuttered. Our health care is too costly; our schools fail too many; and each day brings further evidence that the ways we use energy strengthen our adversaries and threaten our planet. These are the indicators of crisis, subject to data and statistics. Less measurable but no less profound is a sapping of confidence across our land — a nagging fear that America's decline is inevitable, and that the next generation must lower its sights. Today I say to you that the challenges we face are real. They are serious and they are many. They will not be met easily or in a short span of time. But know this, America — they will be met. On this day, we gather because we have chosen hope over fear, unity of purpose over conflict and discord. On this day, we come to proclaim an end to the petty grievances and false promises, the recriminations and worn out dogmas, that for far too long have strangled our politics.
Barack Obama
After simmering years of censorship and repression, the masses finally throng the streets. The chants echoing off the walls to build to a roar from all directions, stoking the courage of the crowds as they march on the center of the capital. Activists inside each column maintain contact with each other via text messages; communications centers receive reports and broadcast them around the city; affinity groups plot the movements of the police via digital mapping. A rebel army of bloggers uploads video footage for all the world to see as the two hosts close for battle. Suddenly, at the moment of truth, the lines go dead. The insurgents look up from the blank screens of their cell phones to see the sun reflecting off the shields of the advancing riot police, who are still guided by close circuits of fully networked technology. The rebels will have to navigate by dead reckoning against a hyper-informed adversary. All this already happened, years ago, when President Mubarak shut down the communications grid during the Egyptian uprising of 2011. A generation hence, when the same scene recurs, we can imagine the middle-class protesters - the cybourgeoisie - will simply slump forward, blind and deaf and wracked by seizures as the microchips in their cerebra run haywire, and it will be up to the homeless and destitute to guide them to safety.
CrimethInc. (Contradictionary)
Deepfakes are built on a technology called generative adversarial networks (GAN). As the name suggests, a GAN is a pair of “adversarial” deep learning neural networks. The first network, the forger network, tries to generate something that looks real, let’s say a synthesized picture of a dog, based on millions of pictures of dogs. The other network, the detective network, compares the forger’s synthesized dog picture with genuine dog pictures, and determines if the forger’s output is real or fake.
Kai-Fu Lee (AI 2041: Ten Visions for Our Future)
In order to construct a flawless imitation, the first step was to gather as much video data as possible with a web crawler. His ideal targets were fashionable Yoruba girls, with their brightly colored V-neck buba and iro that wrapped around their waists, hair bundled up in gele. Preferably, their videos were taken in their bedrooms with bright, stable lighting, their expressions vivid and exaggerated, so that AI could extract as many still-frame images as possible. The object data set was paired with another set of Amaka’s own face under different lighting, from multiple angles and with alternative expressions, automatically generated by his smartstream. Then, he uploaded both data sets to the cloud and got to work with a hyper-generative adversarial network. A few hours or days later, the result was a DeepMask model. By applying this “mask,” woven from algorithms, to videos, he could become the girl he had created from bits, and to the naked eye, his fake was indistinguishable from the real thing. If his Internet speed allowed, he could also swap faces in real time to spice up the fun. Of course, more fun meant more work. For real-time deception to work, he had to simultaneously translate English or Igbo into Yoruba, and use transVoice to imitate the voice of a Yoruba girl and a lip sync open-source toolkit to generate corresponding lip movement. If the person on the other end of the chat had paid for a high-quality anti-fake detector, however, the app might automatically detect anomalies in the video, marking them with red translucent square warnings
Kai-Fu Lee (AI 2041: Ten Visions for Our Future)
Topsy’s execution was a move on an oversized chessboard between two industrial behemoths. Edison’s invention of the light bulb had been only the first step in creating electricity generating stations and the network of wires which took that electricity into every American home to light up the bulbs produced en masse by his own factories. Without control of the generation and distribution of electricity, his bulbs would not have made him King of the Electron. Thus occurred the so-called War of the Currents against his great adversary, George Westinghouse.
Yanis Varoufakis (The Global Minotaur: America, the True Origins of the Financial Crisis and the Future of the World Economy)
generative adversarial network
Parth Detroja (Swipe to Unlock: The Primer on Technology and Business Strategy)
rechecks via a perfection technique called generative adversarial networks (GAN), which will soon make it nearly impossible even for a computer to distinguish the real from the fake.
Thomas Horn (Shadowland: From Jeffrey Epstein to the Clintons, from Obama and Biden to the Occult Elite, Exposing the Deep-State Actors at War with Christianity, Donald Trump, and America's Destiny)
In an age of dynamic malware obfuscation through operations such as mutating hash, a hyper-evolving threat landscape, and technologically next generation adversaries, offensive campaigns have an overwhelming advantage over defensive strategies.
James Scott, Senior Fellow, Institute for Critical Infrastructure Technology
The differences between oil and data are stark, and they offer insight into just how much the world is changing. Oil is valuable because it is scarce; data is valuable because it isn’t. Data is essentially infinite and everyone can get it—creating network effects. Oil is captive to geography, making some countries more powerful than others. Data is unbound by geography, making even powerful countries vulnerable to attack (more on that below). Adversaries cannot turn oil into water or make it look like something it isn’t. But they can with data, corrupting it or generating so much uncertainty that nobody trusts it. Data is simultaneously mighty and weak.
Amy B. Zegart (Spies, Lies, and Algorithms: The History and Future of American Intelligence)